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Beyene KM, Chen DG. Time-dependent receiver operating characteristic curve estimator for correlated right-censored time-to-event data. Stat Methods Med Res 2024; 33:162-181. [PMID: 38130110 DOI: 10.1177/09622802231220496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
In clinical trials, evaluating the accuracy of risk scores (markers) derived from prognostic models for prediction of survival outcomes is of major concern. The time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve are appealing measures to evaluate the predictive accuracy. Several estimation methods have been proposed in the context of classical right-censored data which assumes the event time of individuals are independent. In many applications, however, this may not hold true if, for example, individuals belong to clusters or experience recurrent events. Estimates may be biased if this correlated nature is not taken into account. This paper is then aimed to fill this knowledge gap to introduce a time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve estimation method for right-censored data that take the correlated nature into account. In the proposed method, the unknown status of censored subjects is imputed using conditional survival functions given the marker and frailty of the subjects. An extensive simulation study is conducted to evaluate and demonstrate the finite sample performance of the proposed method. Finally, the proposed method is illustrated using two real-world examples of lung cancer and kidney disease.
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Affiliation(s)
| | - Ding-Geng Chen
- Arizona State University, College of Health Solutions, AZ, USA
- Department of Statistics, University of Pretoria, Pretoria, South Africa
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Yin N, Wang H, Wang Z, Feng K, Xu G, Yin S. A study of brain networks associated with Freezing of gait in Parkinson's disease using transfer entropy analysis. Brain Res 2023; 1821:148610. [PMID: 37783260 DOI: 10.1016/j.brainres.2023.148610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is a common neurodegenerative disease in the elderly. Freezing of Gait (FOG) is one of the common motor symptoms of PD, but the potential mechanism remains unclear. This study aimed to investigate the changes of brain functional network topology in PD patients with FOG. METHODS The resting electroencephalogram (EEG) were acquired from15 PD patients with FOG (PD-FOG), 13 PD patients without FOG (PD-nFOG), and 16 healthy control (HC). Cognitive and motor functions were assessed using subjective scales. The whole-brain functional networks were constructed based on transfer entropy. Transfer entropy was used to analyse the information flow and causality in the network and the network connectivity was analyzed by graph theory. The characteristics of PD-FOG and PD-nFOG were compared by receiver operator characteristic (ROC) curve analysis. RESULTS The θ bands brain network of PD-FOG, PD-nFOG and HC group was significantly different (P < 0.05). The average characteristic path length of the θ bands brain network was positively correlated with FOG Questionnaire (FOGQ). PD-FOG and PD-nFOG get high classification accuracy according to this feature. The information inflow in the frontal and occipital lobes and information outflow in the temporal lobe of PD-FOG patients in the θ bands increased significantly. CONCLUSIONS The whole-brain functional network characteristics of PD-FOG in the θ bands can serve as potential biomarkers for early diagnosis of PD-FOG. Abnormal information flow of the frontal, occipital, and temporal lobes in the θ bands may be an important factor leading to FOG.
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Affiliation(s)
- Ning Yin
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Haili Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Zhaoya Wang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China
| | - Keke Feng
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, China; School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, China.
| | - Shaoya Yin
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300350, China.
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Hapfelmeier A, On BI, Mühlau M, Kirschke JS, Berthele A, Gasperi C, Mansmann U, Wuschek A, Bussas M, Boeker M, Bayas A, Senel M, Havla J, Kowarik MC, Kuhn K, Gatz I, Spengler H, Wiestler B, Grundl L, Sepp D, Hemmer B. Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis. Ther Adv Neurol Disord 2023; 16:17562864231161892. [PMID: 36993939 PMCID: PMC10041597 DOI: 10.1177/17562864231161892] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 02/19/2023] [Indexed: 03/31/2023] Open
Abstract
Background Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about 2.8 million people worldwide. Disease course after the most common diagnoses of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) is highly variable and cannot be reliably predicted. This impairs early personalized treatment decisions. Objectives The main objective of this study was to algorithmically support clinical decision-making regarding the options of early platform medication or no immediate treatment of patients with early RRMS and CIS. Design Retrospective monocentric cohort study within the Data Integration for Future Medicine (DIFUTURE) Consortium. Methods Multiple data sources of routine clinical, imaging and laboratory data derived from a large and deeply characterized cohort of patients with MS were integrated to conduct a retrospective study to create and internally validate a treatment decision score [Multiple Sclerosis Treatment Decision Score (MS-TDS)] through model-based random forests (RFs). The MS-TDS predicts the probability of no new or enlarging lesions in cerebral magnetic resonance images (cMRIs) between 6 and 24 months after the first cMRI. Results Data from 65 predictors collected for 475 patients between 2008 and 2017 were included. No medication and platform medication were administered to 277 (58.3%) and 198 (41.7%) patients. The MS-TDS predicted individual outcomes with a cross-validated area under the receiver operating characteristics curve (AUROC) of 0.624. The respective RF prediction model provides patient-specific MS-TDS and probabilities of treatment success. The latter may increase by 5-20% for half of the patients if the treatment considered superior by the MS-TDS is used. Conclusion Routine clinical data from multiple sources can be successfully integrated to build prediction models to support treatment decision-making. In this study, the resulting MS-TDS estimates individualized treatment success probabilities that can identify patients who benefit from early platform medication. External validation of the MS-TDS is required, and a prospective study is currently being conducted. In addition, the clinical relevance of the MS-TDS needs to be established.
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Affiliation(s)
| | - Begum Irmak On
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität in Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Mark Mühlau
- Department of Neurology, Klinikum rechts der Isar School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, Klinikum rechts der Isar School of Medicine, Technical University of Munich, Munich, Germany
| | - Christiane Gasperi
- Department of Neurology, Klinikum rechts der Isar School of Medicine, Technical University of Munich, Munich, Germany
| | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität in Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Alexander Wuschek
- Department of Neurology, Klinikum rechts der Isar School of Medicine, Technical University of Munich, Munich, Germany
| | - Matthias Bussas
- Department of Neurology, Klinikum rechts der Isar School of Medicine, Technical University of Munich, Munich, Germany
| | - Martin Boeker
- Institute of AI and Informatics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Antonios Bayas
- Department of Neurology, Medical Faculty, University of Augsburg, Augsburg, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Makbule Senel
- Department of Neurology, Ulm University Hospital, Ulm, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Joachim Havla
- Institute of Clinical Neuroimmunology, LMU Hospital, Ludwig-Maximilians-Universität in Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Markus C. Kowarik
- Department of Neurology & Stroke and Hertie-Institute for Clinical Brain Research, Eberhard-Karls University of Tübingen, Tübingen, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Klaus Kuhn
- Institute of AI and Informatics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Ingrid Gatz
- Institute of AI and Informatics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Helmut Spengler
- Institute of AI and Informatics in Medicine, School of Medicine, Technical University of Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Lioba Grundl
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dominik Sepp
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Bernhard Hemmer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- Data Integration for Future Medicine (DIFUTURE) Consortium, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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Beyene KM, El Ghouch A. Time-dependent ROC curve estimation for interval-censored data. Biom J 2022; 64:1056-1074. [PMID: 35523738 DOI: 10.1002/bimj.202000382] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 10/20/2021] [Accepted: 11/07/2021] [Indexed: 11/10/2022]
Abstract
The receiver-operating characteristic (ROC) curve is the most popular graphical method for evaluating the classification accuracy of a diagnostic marker. In time-to-event studies, the subject's event status is time-dependent, and hence, time-dependent extensions of ROC curve have been proposed. However, in practice, the calculation of this curve is not straightforward due to the presence of censoring that may be of different types. Existing methods focus on the more standard and simple case of right-censoring and neglect the general case of mixed interval-censored data that may involve left-, right-, and interval-censored observations. In this context, we propose and study a new time-dependent ROC curve estimator. We also consider some summary measures (area under the ROC curve and Youden index) traditionally associated with ROC as well as the Youden-based cutoff estimation method. The proposed method uses available data very efficiently. To this end, the unknown status (positive or negative) of censored subjects are estimated from the data via the estimation of the conditional survival function given the marker. For that, we investigate both model-based and nonparametric approaches. We also provide variance estimates and confidence intervals using Bootstrap. A simulation study is conducted to investigate the finite sample behavior of the proposed methods and to compare their performance with a competitor. Globally, we observed better finite sample performances for the proposed estimators. Finally, we illustrate the methods using two data sets one from a hypobaric decompression sickness study and the other from an oral health study. The proposed methods are implemented in the R package cenROC.
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Affiliation(s)
- Kassu Mehari Beyene
- ISBA, UCLouvain, Louvain la Neuve, Belgium.,Department of Statistics, College of Natural Sciences, Wollo University, Dessie, Ethiopia
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